class_docstr: MobileNet-V3
  
  A this model utilizes the MobileNet-v3 specific 'efficient head', where global pooling is done before the
  head convolution without a final batch-norm layer before the classifier.
  
  Paper: https://arxiv.org/abs/1905.02244
code: def forward(self, x):
      x = self.features(x)
      x = x.flatten(1)
      if self.drop_rate > 0.0:
          x = F.dropout(x, p=self.drop_rate, training=self.training)
      return self.classifier(x)
  
imports: import torch.nn
  import torch.nn.functional
  from .activations import get_act_fn, get_act_layer, HardSwish
  from .config import layer_config_kwargs
  from .conv2d_layers import select_conv2d
  from .helpers import load_pretrained
  from .efficientnet_builder import *
label_desc: Use image node_id 4 for calling the MobileNetV3 obj's underlying member method code with example usage: obj.forward(x) and returns: self